Computer Vision
Core Machine Learning
Meta-Learning with Shared Amortized Variational Inference
August 13, 2020
Abstract
We propose a novel amortized variational inference scheme for an empirical Bayes meta-learning model, where model parameters are treated as latent variables. We learn the prior distribution over model parameters conditioned on limited training data using a variational autoencoder approach. Our framework proposes sharing the same amortized inference network between the conditional prior and variational posterior distributions over the model parameters. While the posterior leverages both the labeled support and query data, the conditional prior is based only on the labeled support data. We show that in earlier work, relying on Monte-Carlo approximation, the conditional prior collapses to a Dirac delta function. In contrast, our variational approach prevents this collapse and preserves uncertainty over the model parameters. We evaluate our approach on the miniImageNet, CIFAR-FS and FC100 datasets, and present results demonstrating its advantages over previous work.
AUTHORS
Written by
Ekaterina Iakovleva
Jakob Verbeek
Karteek Alahari
Publisher
International Conference on Machine Learning (ICML) 2020
Research Topics
Computer Vision
Core Machine Learning
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